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Cluster Sampling Advantages

The Cluster Sampling Method Has More Advantages Than You Think

Cluster sampling is a method preferred by experienced and professional statistical data analyzers. The cluster sampling advantages are listed below along with some other related information.
MarketingWit Staff
Last Updated: Jun 3, 2018
Cluster sampling is a technique used extensively in market research. It is used when groups can be obviously made in a huge population. Here, the total population is divided into clusters, and a random sample is selected. This is done for every group, and the required data is collected from this sample. This is done for every element of the group. Some cluster sampling advantages are given in this article, along with the uses of this technique and its disadvantages as well.
Vital Information
To understand this technique and its benefits, you need to understand the definitions given below:
  1. Sampling: It is a technique in which certain members of a population group are selected so that they can act as representatives for the entire population.
  2. Sampling Unit: It is the subject on which information is to be collected and kept under observation.
  3. Sampling Fraction: It is a ratio of sampling size and population.
  4. Sampling Frame: It is a list of many units from which any sample is drawn.
  5. Sampling Scheme: It is the procedure by which the unit is drawn from frame.
  6. Cluster Sampling: It is the method in which those units, which are not identified independently but in a group, and are called cluster samples.
  7. Stratified Sampling: In this method, the frames are divided into homogeneous subgroups on basis of a particular attribute (like age or occupation).
As the above definitions tell us, sampling is a process of selecting certain members of population. This is done so that they can act as representatives of that population. Any population, when its size is too big, it is not feasible to take into account each and every member of such population. For the purpose of observation and research, some members are selected so that they can act as representatives of the entire population. The results of observation of any such samples may not be accurate for entire population, but they are considered to be the closest to actual behavior to that population.
  • Reducing field time
  • Reducing costs
  • Increasing accuracy
  • Market research, etc
All the other probabilistic methods require frames of all the units, but the cluster method does not require that. Once the clusters are selected, they are compiled into frames. Now, various probabilistic researches and observations are performed on these frames and required conclusions are drawn.
  • Feasibility: This method takes large populations into account. Since these groups are so large, deploying any other technique would be very difficult task. It is feasible only when you are dealing with large population.
  • Economy: The regular two major concerns of expenditure, i.e., traveling and listing, are greatly reduced in this method. For example: Compiling research information about every house hold in city would be a very difficult, whereas compiling information about various blocks of the city will be easier. Here, traveling as well as listing efforts will be greatly reduced.
  • Reduced Variability: When you are considering the estimates by any other method, reduced variability in results are observed. This may not be an ideal situation every time.
  • Biased Samples: If the group in population that is chosen as a sample has a biased opinion, then the entire population is inferred to have the same opinion. This may not be the actual case.
  • Errors: The other probabilistic methods give fewer errors than this method. For this reason, it is discouraged for beginners.
The above article should help the reader for knowing and understanding some of the concepts of sampling. I hope this article has benefited the readers by increasing their knowledge on statistical data collection and also acquainted them with some advantages of cluster sampling.